Building Applications with Vector Databases

Overview

DeepLearning.AI's short course teaches how to create six exciting applications of vector databases and implement them using Pinecone, covering RAG, recommender systems, and advanced search techniques.

Applications Covered

1. RAG for LLM Enhancement: Augment language models with external knowledge
2. Recommender Systems: Combine semantic search with RAG
3. Hybrid Search: Find items using both images and descriptive text
4. Semantic Search: Implement efficient similarity search
5. Anomaly Detection: Identify outliers in vector spaces
6. Question Answering: Build intelligent Q&A systems

Technical Skills

• Implementing vector databases with Pinecone
• Building RAG pipelines
• Creating hybrid search systems
• Multimodal retrieval techniques
• Semantic similarity search
• Production deployment strategies

Course Format

• Short, focused course from DeepLearning.AI
• Hands-on implementation exercises
• Real-world application examples
• Pinecone integration throughout
• Practical, project-based learning

Target Audience

• ML engineers and data scientists
• AI application developers
• Software engineers building with LLMs
• Technical professionals in AI/ML

Prerequisites

• Basic Python programming
• Understanding of machine learning concepts
• Familiarity with embeddings (helpful but not required)

Pricing

Free course from DeepLearning.AI